Chen, Tianqi, and Carlos Guestrin. 2016.
“XGBoost: A Scalable Tree Boosting System.” In
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–94. New York, NY, USA: ACM; Association for Computing Machinery.
https://doi.org/10.1145/2939672.2939785.
Fang, Zhiyuan, Shuang Yang, Chun Lv, et al. 2022.
“Application of a Data-Driven XGBoost Model for the Prediction of COVID-19 in the USA: A Time-Series Study.” BMJ Open 12 (7): e056685.
https://doi.org/10.1136/bmjopen-2021-056685.
Fomunyam, R. A. 2023.
“The Impact of the u.s. Macroeconomic Variables on the CBOE VIX Index.” Journal of Economics and Finance 47 (1): 77–94.
https://www.proquest.com/docview/2642416749.
Hakkal, Soukaina, and Ayoub Ait Lahcen. 2024.
“XGBoost to Enhance Learner Performance Prediction.” Computers and Education: Artificial Intelligence 7: 100254.
https://doi.org/10.1016/j.caeai.2024.100254.
Hu, Ting, and Ting Song. 2019.
“Research on XGBoost Academic Forecasting and Analysis Modelling.” Journal of Physics: Conference Series 1324 (1): 012091.
https://doi.org/10.1088/1742-6596/1324/1/012091.
Li, H., Y. Cao, S. Li, J. Zhao, and Y. Sun. 2020.
“XGBoost Model and Its Application to Personal Credit Evaluation.” IEEE Intelligent Systems 35 (3): 52–61.
https://doi.org/10.1109/MIS.2020.2972533.
Liew, Xin Yu, Nazia Hameed, and Jeremie Clos. 2021.
“An Investigation of XGBoost-Based Algorithm for Breast Cancer Classification.” Machine Learning with Applications 6: 100154.
https://doi.org/10.1016/j.mlwa.2021.100154.
Nikolaidis, P. T., Beat Knechtle, and other co-authors. 2023.
“Analysis of the 10-Day Ultra-Marathon Using a Predictive XGBoost Model.” Open Sports Sciences Journal 16.
https://uwf-flvc.primo.exlibrisgroup.com/discovery/fulldisplay?docid=cdi_doaj_primary_oai_doaj_org_article_986cc6e5973948ed919ab7ac5176113a.
Saleh, M., E. Amona, M. Kuttikat, I. Sahoo, D. Chan, J. Murphy, and M. Lund. 2024.
“Child Mental Health Predictors Among Camp Tamil Refugees: Utilizing Linear and XGBOOST Models.” PLoS ONE 19 (9): e0303632.
https://doi.org/10.1371/journal.pone.0303632.
Sharma, Anjali, and Wouter J. M. I. Verbeke. 2020.
“Improving Diagnosis of Depression with XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081).” Frontiers in Big Data 3: 15.
https://doi.org/10.3389/fdata.2020.00015.
Sivakumar, R., and S. Elangovan. 2023.
“Prediction of Seasonal Infectious Diseases Based on Hybrid Machine Learning Approach.” International Journal of Health Sciences 7 (2): 1958–69.
https://research.ebsco.com/c/imx7og/viewer/pdf/ff4a3en7vb.
Su, Wenjie, Fei Jiang, Chen Shi, Dapeng Wu, Lihua Liu, Shu Li, Ying Yuan, and Jie Shi. 2023.
“An XGBoost-Based Knowledge Tracing Model.” International Journal of Computational Intelligence Systems.
https://doi.org/10.1007/s44196-023-00192-y.
Wiens, Mark, April Verone-Boyle, Nate Henscheid, J. T. Podichetty, and John Burton. 2025.
“A Tutorial and Use Case Example of the eXtreme Gradient Boosting (XGBoost) Artificial Intelligence Algorithm for Drug Development Applications.” Clinical and Translational Science 18: e70172.
https://doi.org/10.1111/cts.70172.
Xu, Xiao-Ming, Yang S. Liu, Su Hong, Chuan Liu, Jun Cao, Xiao-Rong Chen, Zhen Lv, et al. 2024.
“The Prediction of Self-Harm Behaviors in Young Adults with Multi-Modal Data: An XGBoost Approach.” Journal of Affective Disorders Reports 16: 100723.
https://doi.org/10.1016/j.jadr.2024.100723.
Zhang, Ping, Yibing Jia, and Yanan Shang. 2022.
“Research and Application of XGBoost in Imbalanced Data.” International Journal of Distributed Sensor Networks 18 (6).
https://doi.org/10.1177/15501329221106935.